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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Velocity and Position by Graphical Method01:34

Velocity and Position by Graphical Method

Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to calculate...
Position Vectors01:29

Position Vectors

A position vector is a fundamental concept in mathematics that helps determine the position of one point with respect to another point in space. It is a vector that describes the direction and distance between two points. Position vectors are highly useful in the field of math and science, as they help represent spatial relationships and make calculations easier.
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Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

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Related Experiment Videos

Adaptive Weighted Factor Graph Optimized Positioning Algorithm Based on Joint GNSS/INS/Vision Residual Detection.

Jin Wang1, Jun Zou1, Yan Xing2

  • 1School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive weighted localization algorithm for multi-sensor fusion (GNSS, IMU, vision). It enhances positioning accuracy and robustness in complex environments for urban IoT and autonomous driving.

Keywords:
GNSS/INS/visionadaptive weightingfactor graph optimizationjoint residual

Related Experiment Videos

Area of Science:

  • Robotics and Autonomous Systems
  • Sensor Fusion and Navigation

Background:

  • Multi-sensor fusion (GNSS, IMU, vision) is crucial for accurate positioning in urban IoT and automated driving.
  • Conventional fixed-weight algorithms struggle with environmental sensor error fluctuations, limiting performance.

Purpose of the Study:

  • To develop a novel multi-sensor adaptive weighted localization algorithm for improved robustness and accuracy.
  • To dynamically adjust sensor weights to mitigate the impact of environmental changes and sensor errors.

Main Methods:

  • Proposed a novel algorithm using joint residuals detection via sliding window accumulation of sensor measurements.
  • Integrated a global weight decay factor into M-estimation for dynamic sensor weight adjustment.
  • Suppressed outliers to enhance state estimation accuracy for position, velocity, and attitude.

Main Results:

  • Demonstrated superior performance on GNSS-Visual-Inertial Navigation System (GVINS) datasets in challenging scenarios (weak GNSS, IMU drift).
  • Achieved a 32.3% improvement in positioning accuracy and a 32% reduction in velocity error compared to traditional algorithms.
  • Effectively mitigated error accumulation and maintained stable estimation during GNSS signal interference.

Conclusions:

  • The proposed adaptive weighted algorithm offers high-precision and robust state estimation in complex environments.
  • It significantly enhances positioning performance for urban IoT and automated driving applications.
  • The method proves highly suitable for dynamic scenarios with unreliable sensor data.